cBioPortalData: User Guide
July 22, 2025
Contents
Installation
library(cBioPortalData)
library(AnVIL)Introduction
The cBioPortal for Cancer Genomics website is a great resource for interactive exploration of study datasets. However, it does not easily allow the analyst to obtain and further analyze the data.
We’ve developed the cBioPortalData package to fill this need to
programmatically access the data resources available on the cBioPortal.
The cBioPortalData package provides an R interface for accessing the
cBioPortal study data within the Bioconductor ecosystem.
It downloads study data from the cBioPortal API (the full API specification can be found here https://cbioportal.org/api) and uses Bioconductor infrastructure to cache and represent the data.
We demonstrate common use cases of cBioPortalData and curatedTCGAData
during Bioconductor conference
workshops.
We use the MultiAssayExperiment (Ramos et al. 2017) package to integrate,
represent, and coordinate multiple experiments for the studies available in the
cBioPortal. This package in conjunction with curatedTCGAData give access to
a large trove of publicly available bioinformatic data. Please see our
JCO Clinical Cancer Informatics publication (Ramos et al. 2020).
Citations
Our free and open source project depends on citations for funding. When using
cBioPortalData, please cite the following publications:
citation("MultiAssayExperiment")
citation("cBioPortalData")Overview
Data Structures
Data are provided as a single MultiAssayExperiment per study. The
MultiAssayExperiment representation usually contains SummarizedExperiment
objects for expression data and RaggedExperiment objects for mutation and
CNV-type data. RaggedExperiment is a data class for representing ‘ragged’
genomic location data, meaning that the measurements per sample vary.
For more information, please see the RaggedExperiment and
SummarizedExperiment vignettes.
Identifying available studies
As we work through the data, there are some datasest that cannot be represented
as MultiAssayExperiment objects. This can be due to a number of reasons such
as the way the data is handled, presence of mis-matched identifiers, invalid
data types, etc. To see what datasets are currently not building, we can
look refer to getStudies() with the buildReport = TRUE argument.
cbio <- cBioPortal()
studies <- getStudies(cbio, buildReport = TRUE)
head(studies)## # A tibble: 6 × 15
## name description publicStudy pmid citation groups status importDate
## <chr> <chr> <lgl> <chr> <chr> <chr> <int> <chr>
## 1 Acute Lymphob… Comprehens… TRUE 2573… Anderss… "PUBL… 0 2024-12-0…
## 2 Adenoid Cysti… Targeted S… TRUE 2441… Ross et… "ACYC… 0 2024-12-0…
## 3 Adenoid Cysti… Whole-geno… TRUE 2682… Drier e… "ACYC" 0 2024-12-0…
## 4 Appendiceal C… Targeted s… TRUE 3649… Michael… "PUBL… 0 2024-12-0…
## 5 Bladder Cance… Whole exom… TRUE 2690… Al-Ahma… "" 0 2024-12-0…
## 6 Bladder Cance… Genomic Pr… TRUE 2509… Kim et … "PUBL… 0 2024-12-0…
## # ℹ 7 more variables: allSampleCount <int>, readPermission <lgl>,
## # studyId <chr>, cancerTypeId <chr>, referenceGenome <chr>, api_build <lgl>,
## # pack_build <lgl>The last two columns will show the availability of each studyId for
either download method (pack_build for cBioDataPack and api_build for
cBioPortalData).
Choosing download method
There are two main user-facing functions for downloading data from the cBioPortal API.
cBioDataPackmakes use of the tarball distribution of study data. This is useful when the user wants to download and analyze the entirety of the data as available from the cBioPortal.org website.cBioPortalDataallows a more flexibile approach to obtaining study data based on the available parameters such as molecular profile identifiers. This option is useful for users who have a set of gene symbols or identifiers and would like to get a smaller subset of the data that correspond to a particular molecular profile.
Two main functions
cBioDataPack: Obtain Study Data as Zipped Tarballs
This function will access the packaged data from
https://cbioportal.org/datasets and return an integrative
MultiAssayExperiment representation.
## Use ask=FALSE for non-interactive use
laml <- cBioDataPack("laml_tcga", ask = FALSE)
laml## A MultiAssayExperiment object of 12 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 12:
## [1] cna: SummarizedExperiment with 24776 rows and 191 columns
## [2] cna_hg19.seg: RaggedExperiment with 13571 rows and 191 columns
## [3] linear_cna: SummarizedExperiment with 24776 rows and 191 columns
## [4] methylation_hm27: SummarizedExperiment with 10968 rows and 194 columns
## [5] methylation_hm450: SummarizedExperiment with 10968 rows and 194 columns
## [6] mrna_seq_rpkm: SummarizedExperiment with 19720 rows and 179 columns
## [7] mrna_seq_rpkm_zscores_ref_all_samples: SummarizedExperiment with 19720 rows and 179 columns
## [8] mrna_seq_rpkm_zscores_ref_diploid_samples: SummarizedExperiment with 19719 rows and 179 columns
## [9] mrna_seq_v2_rsem: SummarizedExperiment with 20531 rows and 173 columns
## [10] mrna_seq_v2_rsem_zscores_ref_all_samples: SummarizedExperiment with 20531 rows and 173 columns
## [11] mrna_seq_v2_rsem_zscores_ref_diploid_samples: SummarizedExperiment with 20440 rows and 173 columns
## [12] mutations: RaggedExperiment with 2584 rows and 197 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filescBioPortalData: Obtain data from the cBioPortal API
This function provides a more flexible and granular way to request a
MultiAssayExperiment object from a study ID, molecular profile, set of genes,
and a sample list.
In this example, we will obtain data from the Adrenocortical carcinoma (ACC;
acc_tcga) study. The list of genes below are based on potential driving
alterations including amplifications, deletions, and point mutations. Also
included are a set of genes known to initiate familial syndromes including
adrenocortical neoplasms as described in Zheng et al. (2016).
acc <- cBioPortalData(
api = cbio,
by = "hugoGeneSymbol",
studyId = "acc_tcga",
genes = c(
"TERT", "TERF2", "CDK4", "ZNRF3", "CDKN2A", "RB1", "RPL22",
"TP53", "CTNNB1", "PRKAR1A", "MEN1"
),
molecularProfileIds = c("acc_tcga_linear_CNA", "acc_tcga_mutations"),
)## harmonizing input:
## removing 1 colData rownames not in sampleMap 'primary'acc## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] acc_tcga_mutations: RangedSummarizedExperiment with 54 rows and 37 columns
## [2] acc_tcga_linear_CNA: SummarizedExperiment with 11 rows and 90 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesNote. To avoid overloading the API service, the API was designed to only query a part of the study data. Therefore, the user is required to enter either a set of genes of interest or a gene panel identifier.
Considerations
Note that cBioPortalData and cBioDataPack obtain data diligently curated
by the cBio Portal data team. The original data and curation lies in the
https://github.com/cBioPortal/cBioPortal GitHub repository. However, despite
the curation efforts there may be some inconsistencies in identifiers
in the data. This causes our software to not work as intended though we have
made efforts to represent all the data from both API and tarball formats.
metadata
You may notice that the metadata() may have some additional data that was
not able to be integrated in the MultiAssayExperiment.
metadata(acc)## [[1]]
## # A tibble: 62 × 22
## uniqueSampleKey uniquePatientKey molecularProfileId patientId entrezGeneId
## <chr> <chr> <chr> <chr> <int>
## 1 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… acc_tcga_mutations TCGA-OR-… 7157
## 2 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… acc_tcga_mutations TCGA-OR-… 1499
## 3 VENHQS1PUi1BNUozL… VENHQS1PUi1BNUo… acc_tcga_mutations TCGA-OR-… 5573
## 4 VENHQS1PUi1BNUo1L… VENHQS1PUi1BNUo… acc_tcga_mutations TCGA-OR-… 7157
## 5 VENHQS1PUi1BNUo1L… VENHQS1PUi1BNUo… acc_tcga_mutations TCGA-OR-… 6146
## 6 VENHQS1PUi1BNUo1L… VENHQS1PUi1BNUo… acc_tcga_mutations TCGA-OR-… 4221
## 7 VENHQS1PUi1BNUo1L… VENHQS1PUi1BNUo… acc_tcga_mutations TCGA-OR-… 84133
## 8 VENHQS1PUi1BNUo4L… VENHQS1PUi1BNUo… acc_tcga_mutations TCGA-OR-… 7157
## 9 VENHQS1PUi1BNUpBL… VENHQS1PUi1BNUp… acc_tcga_mutations TCGA-OR-… 4221
## 10 VENHQS1PUi1BNUpBL… VENHQS1PUi1BNUp… acc_tcga_mutations TCGA-OR-… 7157
## # ℹ 52 more rows
## # ℹ 17 more variables: studyId <chr>, center <chr>, mutationStatus <chr>,
## # validationStatus <chr>, tumorAltCount <int>, tumorRefCount <int>,
## # normalAltCount <int>, normalRefCount <int>, referenceAllele <chr>,
## # proteinChange <chr>, variantType <chr>, keyword <chr>, variantAllele <chr>,
## # refseqMrnaId <chr>, proteinPosStart <int>, proteinPosEnd <int>, type <chr>
##
## [[2]]
## # A tibble: 990 × 7
## uniqueSampleKey uniquePatientKey entrezGeneId molecularProfileId patientId
## <chr> <chr> <int> <chr> <chr>
## 1 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 1019 acc_tcga_linear_C… TCGA-OR-…
## 2 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 1029 acc_tcga_linear_C… TCGA-OR-…
## 3 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 1499 acc_tcga_linear_C… TCGA-OR-…
## 4 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 4221 acc_tcga_linear_C… TCGA-OR-…
## 5 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 5573 acc_tcga_linear_C… TCGA-OR-…
## 6 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 5925 acc_tcga_linear_C… TCGA-OR-…
## 7 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 6146 acc_tcga_linear_C… TCGA-OR-…
## 8 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 7014 acc_tcga_linear_C… TCGA-OR-…
## 9 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 7015 acc_tcga_linear_C… TCGA-OR-…
## 10 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 7157 acc_tcga_linear_C… TCGA-OR-…
## # ℹ 980 more rows
## # ℹ 2 more variables: studyId <chr>, type <chr>Build prompts
You will also get a message for studyIds whose data has not been fully
integrated into a MultiAssayExperiment.
## Our testing shows that '%s' is not currently building.
## Use 'downloadStudy()' to manually obtain the data.
## Proceed anyway? [y/n]: yManual downloads
For this reason, we have also provided the downloadStudy, untarStudy, and
loadStudy functions to allow researchers to simply download the data and
potentially, manually curate it. Generally, we advise researchers to report
inconsistencies in the data in the cBioPortal data repository.
Clearing the cache
cBioDataPack
In cases where a download is interrupted, the user may experience a corrupt
cache. The user can clear the cache for a particular study by using the
removeCache function. Note that this function only works for data downloaded
through the cBioDataPack function.
removeCache("laml_tcga")cBioPortalData
For users who wish to clear the entire cBioPortalData cache, it is
recommended that they use:
unlink("~/.cache/cBioPortalData/")Example Analysis: Kaplan-Meier Plot
We can use information in the colData to draw a K-M plot with a few
variables from the colData slot of the MultiAssayExperiment. First, we load
the necessary packages:
library(survival)
library(survminer)We can check the data to lookout for any issues.
table(colData(laml)$OS_STATUS)##
## 0:LIVING 1:DECEASED
## 67 133class(colData(laml)$OS_MONTHS)## [1] "character"Now, we clean the data a bit to ensure that our variables are of the right type for the subsequent survival model fit.
collaml <- colData(laml)
collaml[collaml$OS_MONTHS == "[Not Available]", "OS_MONTHS"] <- NA
collaml$OS_MONTHS <- as.numeric(collaml$OS_MONTHS)
colData(laml) <- collamlWe specify a simple survival model using SEX as a covariate and we draw
the K-M plot.
fit <- survfit(
Surv(OS_MONTHS, as.numeric(substr(OS_STATUS, 1, 1))) ~ SEX,
data = colData(laml)
)
ggsurvplot(fit, data = colData(laml), risk.table = TRUE)
Data update requests
If you are interested in a particular study dataset that is not currently building, please open an issue at our GitHub repository and we will do our best to resolve the issues with the code base. Data issues can be opened at the cBioPortal data repository.
We appreciate your feedback!
sessionInfo
Click to see session info
sessionInfo()## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
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## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
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## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
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## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] survminer_0.5.0 ggpubr_0.6.1
## [3] ggplot2_3.5.2 survival_3.8-3
## [5] cBioPortalData_2.21.5 MultiAssayExperiment_1.35.5
## [7] SummarizedExperiment_1.39.1 Biobase_2.69.0
## [9] GenomicRanges_1.61.1 Seqinfo_0.99.2
## [11] IRanges_2.43.0 S4Vectors_0.47.0
## [13] BiocGenerics_0.55.0 generics_0.1.4
## [15] MatrixGenerics_1.21.0 matrixStats_1.5.0
## [17] AnVIL_1.21.8 AnVILBase_1.3.1
## [19] dplyr_1.1.4 BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_2.0.0
## [3] magrittr_2.0.3 magick_2.8.7
## [5] GenomicFeatures_1.61.5 farver_2.1.2
## [7] rmarkdown_2.29 BiocIO_1.19.0
## [9] vctrs_0.6.5 memoise_2.0.1
## [11] Rsamtools_2.25.1 RCurl_1.98-1.17
## [13] tinytex_0.57 rstatix_0.7.2
## [15] htmltools_0.5.8.1 S4Arrays_1.9.1
## [17] BiocBaseUtils_1.11.2 lambda.r_1.2.4
## [19] curl_6.4.0 broom_1.0.8
## [21] Formula_1.2-5 SparseArray_1.9.1
## [23] sass_0.4.10 bslib_0.9.0
## [25] htmlwidgets_1.6.4 httr2_1.2.1
## [27] zoo_1.8-14 futile.options_1.0.1
## [29] cachem_1.1.0 commonmark_2.0.0
## [31] GenomicAlignments_1.45.1 mime_0.13
## [33] lifecycle_1.0.4 pkgconfig_2.0.3
## [35] Matrix_1.7-3 R6_2.6.1
## [37] fastmap_1.2.0 shiny_1.11.1
## [39] digest_0.6.37 GCPtools_0.99.2
## [41] RaggedExperiment_1.33.7 AnnotationDbi_1.71.0
## [43] ps_1.9.1 RSQLite_2.4.2
## [45] labeling_0.4.3 filelock_1.0.3
## [47] RTCGAToolbox_2.39.0 km.ci_0.5-6
## [49] RJSONIO_2.0.0 httr_1.4.7
## [51] abind_1.4-8 compiler_4.5.1
## [53] bit64_4.6.0-1 withr_3.0.2
## [55] backports_1.5.0 BiocParallel_1.43.4
## [57] carData_3.0-5 DBI_1.2.3
## [59] ggsignif_0.6.4 rappdirs_0.3.3
## [61] DelayedArray_0.35.2 rjson_0.2.23
## [63] tools_4.5.1 chromote_0.5.1
## [65] httpuv_1.6.16 glue_1.8.0
## [67] restfulr_0.0.16 promises_1.3.3
## [69] gridtext_0.1.5 grid_4.5.1
## [71] gtable_0.3.6 KMsurv_0.1-6
## [73] tzdb_0.5.0 tidyr_1.3.1
## [75] websocket_1.4.4 data.table_1.17.8
## [77] hms_1.1.3 car_3.1-3
## [79] xml2_1.3.8 utf8_1.2.6
## [81] XVector_0.49.0 markdown_2.0
## [83] pillar_1.11.0 stringr_1.5.1
## [85] later_1.4.2 splines_4.5.1
## [87] ggtext_0.1.2 BiocFileCache_2.99.5
## [89] lattice_0.22-7 rtracklayer_1.69.1
## [91] bit_4.6.0 tidyselect_1.2.1
## [93] Biostrings_2.77.2 miniUI_0.1.2
## [95] knitr_1.50 gridExtra_2.3
## [97] litedown_0.7 bookdown_0.43
## [99] futile.logger_1.4.3 xfun_0.52
## [101] DT_0.33 stringi_1.8.7
## [103] UCSC.utils_1.5.0 yaml_2.3.10
## [105] evaluate_1.0.4 codetools_0.2-20
## [107] tibble_3.3.0 BiocManager_1.30.26
## [109] cli_3.6.5 xtable_1.8-4
## [111] processx_3.8.6 jquerylib_0.1.4
## [113] survMisc_0.5.6 dichromat_2.0-0.1
## [115] Rcpp_1.1.0 GenomeInfoDb_1.45.9
## [117] GenomicDataCommons_1.33.1 dbplyr_2.5.0
## [119] png_0.1-8 XML_3.99-0.18
## [121] rapiclient_0.1.8 parallel_4.5.1
## [123] TCGAutils_1.29.5 readr_2.1.5
## [125] blob_1.2.4 bitops_1.0-9
## [127] scales_1.4.0 purrr_1.1.0
## [129] crayon_1.5.3 rlang_1.1.6
## [131] KEGGREST_1.49.1 rvest_1.0.4
## [133] formatR_1.14References
Ramos, Marcel, Ludwig Geistlinger, Sehyun Oh, Lucas Schiffer, Rimsha Azhar, Hanish Kodali, Ino de Bruijn, et al. 2020. “Multiomic Integration of Public Oncology Databases in Bioconductor.” JCO Clin Cancer Inform 4: 958–71. https://doi.org/10.1200/CCI.19.00119.
Ramos, Marcel, Lucas Schiffer, Angela Re, Rimsha Azhar, Azfar Basunia, Carmen Rodriguez, Tiffany Chan, et al. 2017. “Software for the Integration of Multiomics Experiments in Bioconductor.” Cancer Res. 77 (21): e39–e42. https://doi.org/10.1158/0008-5472.CAN-17-0344.
Zheng, Siyuan, Andrew D Cherniack, Ninad Dewal, Richard A Moffitt, Ludmila Danilova, Bradley A Murray, Antonio M Lerario, et al. 2016. “Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma.” Cancer Cell 29 (5): 723–36. https://doi.org/10.1016/j.ccell.2016.04.002.